Asian Plant Research Journal

6(4): 8-18, 2020; Article no.APRJ.59032 ISSN: 2581-9992

Assessment of Phytoecological Parameters of Forest Massifs in the in the Democratic Republic of the Congo

Y. B. da-Musa Masens1, Koto-te-Nyiwa Ngbolua1,2, Mandung Masens3, John Makiadi Tambu Tembeni1 and Gédéon Ngiala Bongo1*

1Faculty of Science, University of , Kinshasa XI, Kinshasa, Democratic Republic of the Congo. 2University of Gbadolite, Gbadolite, Province of Nord-Ubangi, Democratic Republic of the Congo. 3Higher Institute of Medical Techniques of Lumbi, Province of Kwilu, Democratic Republic of the Congo.

Authors’ contributions

This work was carried out in collaboration among all authors. Author YBMM designed the study. Authors KNN and GNB wrote the first draft of the manuscript. Authors YBMM, KNN and JMTT managed the analyses of the study. Authors MM and GNB managed the literature searches. All authors read and approved the final manuscript.

Article Information

DOI: 10.9734/APRJ/2020/v6i430134 Editor(s): (1) Dr. K. S. Vinayaka, Kumadvathi First Grade College, India. Reviewers: (1) Tej Narayan Mandal, Tribhuvan University, Nepal. (2) Weydson Nyllys Cavalcante Raulino, University of Brasília, Brazil. (3) S. Jalajakshi, Bangalore Central University, India. Complete Peer review History: http://www.sdiarticle4.com/review-history/59032

Received 01 June 2020 Accepted 06 August 2020 Original Research Article Published 12 November 2020

ABSTRACT

Rainforests constitute the great green heart of Africa and thus they present a unique combination of ecological, climatic and human interactions. The degradation of various forest ecosystems in mainly tropical regions and more particularly in sub-Saharan Africa has been a constant reality since the emergence of man. Faced to this almost alarming picture, most often linked to the countless economic challenges and calamitous management and confronted with unavoidable climate change, it seemed appropriate and imperative to assess the quantities of Air Biomass, sequestered carbon stocks, carbon equivalent and basal area produced by various forest massifs encountered in the Kwilu province. This study was carried out at the Kiyaka Agricultural Research Station ______

*Corresponding author: E-mail: [email protected];

Masens et al.; APRJ, 6(4): 8-18, 2020; Article no.APRJ.59032

located in the south of . Five blocks of 100 m / 20 m were delineated using a 200 m long nylon wire divided into 10 m intervals. The system thus delimited made it possible to inventory all the trees, of which dbh measured at 1.30 m at chest height is ≥ 10 cm. The findings showed that species are divided into 11 orders, 42 genera and 19 families. The following systematic units are best represented in terms of number of plant species. These families are namely: Fabaceae, Malvaceae, Apocynaceae, Euphorbiaceae and Olacaceae, which together contain 26 plant species, while nine families are poorly represented. However, this reserve is threatened by its occupants on all sides; they do not want to clear large areas of forest for their field work. It is therefore urgent for INERA/Kiyaka officials to take the necessary measures for its full protection.

Keywords: Phytoecology; forest massifs; Kwilu; Democratic Republic of the Congo.

1. INTRODUCTION (DRC) to access and gain carbon credits to an extend that this would participate in the The rainforests are the great green heart of temperature regulation of the atmosphere. It Africa, and present a unique combination of should be noted that forests sequester and store ecological, climatic and human interactions [1]. more carbon than any other terrestrial ecosystem Recently, there has been a surge of interest in and are an important natural brake on climate tropical forests, as there is increased change. This regulation would then allow to the appreciation of the rich biodiversity they host and reduction of currently increasing greenhouse the many roles they play in the functioning of the gases emissions as stipulated in the Kyoto Earth system at local, regional and global scales protocol [6]. The aim of this study was to assess [1]. However, Forests constitute an efficient different parameters, which can help in the carbon sink through actions like afforestation, preservation of existing forests and limits the forest management, forest management [2]. The emission of greenhouse gases. degradation of various forest ecosystems in tropical regions and more particularly in sub- 2. METHODS Saharan Africa has been a constant reality for decades [3]. 2.1 Study Area

It was reported that "more than 200,000 years This study was carried out at the Kiyaka ago, men controlled fire and used it to burn dry Agricultural Research Station, which is located at tropical rainforests, most often to hunt game or 75 km in the South of Kikwit city in the drive away predators [4]. Currently, there are province and it covered 3250 ha. Its geographical only less than 8% left and the majority of them coordinates are presented as follows: 5° 16' and are located in mountainous and/or difficult to 5° 24' South latitude, 18° 57' and 18° 43' East access areas. Faced to this alarming picture, longitude. The altitude varies between 400 to 509 most often linked to the countless economic m in the valley and 735 m at plateau level. challenges and calamitous management. This management is confronted to the unavoidable 2.2 Methods climate change issue, which seemed appropriate and imperative to assess the quantities of Above Five blocks of 100 m/20 m were delineated using ground Biomass (AGB), sequestered carbon a 200 m long nylon wire divided into 10 m stocks, carbon equivalent and basal area intervals. With this delimitation, it was possible to produced by various forest massifs) encountered inventory all trees, of which the dbh measured in the (Kwilu province). It is 1.30 m from the chest height. Thus, each species necessary to know that the implementation of having a dbh ≥ 10 cm was identified and with improved management practices to build up GPS, the position along with geographical carbon stocks in terrestrial ecosystems is being a coordinates was collected. proven technology to reduce the concentration of Carbon dioxide in the atmosphere [5]. Since we could not directly measure the diameter of each of these trees in the field, hence, the The knowledge of these parameters, in particular circumference was measured at 1.30 m from the sequestered carbon and its equivalent, the ground. For shafts with buttresses and/or aboveground biomass and basal area produced, deformations at this level, these measurements will help the Democratic Republic of the Congo were taken above the buttress or deformation.

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Fig. 1. Map of Kiyaka Agricultural Research Center

For botanical characterization indices, including 3. RESULTS AND DISCUSSION relative species density (DeR) and relative family density (DfR), were calculated as described by 3.1 The Flower of the Forest Massif Masens [7]. Studied

= 100 (1) The floristic inventory listed 48 species, which were distributed into 11 orders, 19 families and = (2) 42 genera. The families of Fabaceae, Malvaceae, Apocynaceae, Euphorbiaceae and Olacaceae had each 26 species. While the The diameter classes of all species with a dbh ˃ following nine families, Annonaceae, Ebenaceae, 10 cm present in different surveys were Irvingiaceae, Lecythidaceae, Cecropiaceae, measured using a tape measure and grouped Rubiaceae, Sapotaceae, Ulmaceae and into different classes. Eco-sociological groups Verbenaceae were poorly represented. were differentiated as described in previous studies. Leaf size types were defined from Table 1 presents the floristic list of the Raunkiaer's classification [8,9]. For biological studied area along with different parameters types (BT), we used Raunkiaer's classification used. (1934), adapted to tropical regions by Belesi and Habari along with the types of diaspore dissemination were determined [8,9]. The floristic inventory listed 48 species, which were distributed into 11 orders, 19 families and 2.3 Data Analysis 42 genera. The families of Fabaceae, Malvaceae, Apocynaceae, Euphorbiaceae and The statistical analyses of the data were carried Olacaceae had each 26 species. While these out using the SPSS 14.0 software. It allowed us families, Ebenaceae, Rubiaceae, Ulmaceae and to know if the p-value value is ≤ 0.05, the Verbenaceae were poorly represented in terms parameters compared are correlated and if the p- of species. value values are ≥ 0.05, we conclude that there is no correlation between the parameters taken 3.2 The Relative Density into account. The ArcGIS and QGIS software were used to produce the map and to spatially The relative density of collected specimens in the distribute the species. study area is presented in the Table 2.

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Table 1. Floristic inventory of Kiyaka massif forest

Scientific names of species Number of dbh ST (m2) Aerial biomass Stored Carbon (t/ha) Equivalent C (t/ha) specimens (cm) (t/ha) Chave Pearson Chave Pearson Chave Pearson Alstonia boonei 12 218 0,35 13,23 10,6 6,22 4,98 22,81 18,27 Angylocalyx margininervatus 5 108 0,22 9,86 5,8 4,63 2,73 16,99 9,99 Anonidium mannii 8 421 1,63 100,13 31,52 47,06 14,81 172,58 54,32 Barteria nigritiana var. fistulosa 5 66 0,07 2,03 2,11 0,95 0,99 3,49 3,64 Brachystegia laurentii 471 814,7 15,38 651,44 405,23 306,18 190,46 1122,75 698,41 Canarium schweinfurthii 7 322 3,81 34,54 47,69 39,73 22,42 145,7 82,21 Carapa procera 2 143 1,24 59,34 14,76 27,89 6,94 102,27 25,43 Ceiba pentandra 3 103 0,32 17,57 6,61 8,26 3,11 30,29 11,39 Celtis mildbraedii 9 273 0,67 31,28 14,75 14,7 6,99 53,92 25,42 Chrysophyllum lacourtianum 15 398 0,91 41,99 25,71 19,74 12,08 72,38 44,31 Cola acuminata 1 11 0,01 0,27 0,29 0,13 0,13 0,47 0,49 Cola bruneelii 8 247 0,55 24,87 13,84 11,69 6,22 42,87 22,82 Cola diversifolia 2 25 0,02 0,68 0,71 0,32 6,22 1,17 1,22 Cola griseiflora 2 42 0,08 3,09 1,87 1,45 0,88 5,33 3,22 Croton sylvaticus 1 16 0,02 0,69 0,73 0,33 0,34 1,2 1,25 Dacryodes klaineana 6 111 0,18 6,83 5,37 3,21 2,52 11,77 9,25 Dichostemma glauscens 8 325 1,06 57,38 19,39 26,97 5,11 98,89 33,42 Diospyros conocarpa 1 14 0,02 0,48 0,5 0,22 0,24 0,83 0,87 Duboscia viridiflora 3 201 1,96 70,98 22,12 33,36 10,39 122,33 38,12 Entandrophragma cylindricum 9 261 0,6 27,05 14,18 12,71 6,66 46,62 24,43 Entandrophragma utile 5 562 5,11 355,15 46,49 166,85 21,85 612,09 80,13 Funtumia africana 1 22 0,04 1,49 1,54 0,69 0,72 2,56 2,66 Garcinia kola 5 197 0,83 33,91 16,91 15,96 7,95 58,53 29,14 Hallea stipulosa 2 39 0,06 2,13 2,22 1 1,04 3,68 3,83 Hollarhena floribunda 10 147 0,18 5,68 5,92 2,67 2,78 9,79 10,21 Homalium longistylum 3 158 0,67 41,46 13,81 19,49 6,49 71,46 23,81 Irvingia robur 1 46 0,17 9,62 3,24 4,52 1,52 16,58 5,58 Mammea africana 2 35 0,05 1,82 1,89 0,85 0,89 3,14 3,26 Musanga cecropioides 4 179 0,63 35,31 11,91 16,59 5,59 60,86 20,52 Olax archesoniana 2 86 0,26 13,51 4,58 6,35 2,15 23,29 7,89

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Scientific names of species Number of dbh ST (m2) Aerial biomass Stored Carbon (t/ha) Equivalent C (t/ha) specimens (cm) (t/ha) Chave Pearson Chave Pearson Chave Pearson Olax sp 1 58 0,21 13,33 4,45 6,26 2,09 22,97 7,68 Oncoba welwitschii 3 16 0,69 43,9 14,6 20,63 8,86 75,67 25,16 Pentaclethra ma crophylla 4 12 0,33 15,89 7,96 7,47 3,74 27,39 13,71 Petersianthus macrocarpus 5 209 0,71 39,12 13,19 18,38 6,2 67,42 22,74 Piptadeniastrum africanum 1 153 1,83 65,9 18,27 30,97 8,56 113,58 31,39 Plagiostyle africana 4 69 0,1 3,77 2,83 1,77 1,33 6,49 4,88 Prioria balsamifera 4 18 5,43 330,59 58,78 155,38 27,62 569,77 101,3 Pycnanthus angolensis 4 259 1,33 63,42 29,9 29,81 14,05 109,31 51,53 Ricinodendron heudelotii var. africanum 1 124 1,21 58,34 13,71 27,42 6,44 100,54 83,63 Scorodophloeus zenkeri 4 297 1,76 90,15 38,26 42,37 17,98 155,38 65,95 Staudtia kamerounensis 4 203 0,83 50,47 16,86 23,72 7,92 86,99 29,05 Sterculia tracagantha 8 157 0,33 15,46 8,57 7,26 4,03 26,64 14,78 Strombosia pustulata 3 76 0,15 6,23 4,11 2,93 1,93 10,74 7,08 Strombosia sp 1 77 0,46 17,35 11,32 8,16 5,32 29,92 19,51 Symphonia globulifera 4 153 0,47 24,81 8,89 11,65 4,18 42,75 15,32 Tabernaemontana crassa 3 88 0,21 10,09 4,81 4,74 2,26 17,4 8,29 Tetrapleura tetraptera 3 168 0,9 39,43 17,48 18,53 8,21 67,96 30,13 Vitex welwitschii 2 81 0,26 13,67 4,63 6,42 2,18 23,56 7,98 Total 201 167,93 54,31 2555,73 1030,91 1224,59 488,1 4491,12 1835,62

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Table 2. Relative density of collected in the study area

Scientific names Number of Relative frequency specimens (%) Brachystegia laurentii(De Wild.) Louis 471 70,09 Chrysophyllum lacourtianum De Wild. 15 2,33 Alstonia boonei De Wild. 12 1,78 Hollarhena floribunda (G. Don) Dur. & Schinz. 10 1,49 Celtis mildbraedii Engl. 9 1,34 Entandrophrama cylindricum Sprague 9 1,34 Anonidium mannii (Oliv.) Engl. et Diels 8 1,19 Cola bruneelii De Wild. 8 1,19 Dichostemma glauscens Pierre 8 1,19 Sterculia tracagantha Lindl. 8 1,19 Canarium schweinfurhtii Engl. 7 1,04 Dacryodes klaineana (Pierre) H.L. Lam. 6 0,89 Angylocalyx margininervatus (Baker.) Baker. 5 0,74 Barteria nigritiana Hook. f. 5 0,74 Entandrophragma utile Sprague 5 0,74 Garcinia kola Heckel 5 0,74 Petersianthus macrocarpus (P. Beauv.) Liben 5 0,74 Musanga cecropioides R. Br. 4 0,59 Pentaclethra macrophylla Benth. 4 0,59 Plagiostyle africana (Mül.-Arg.) Prain 4 0,59 Prioria balsamifera (Harms) Breteler. comb nov. 4 0,59 Pycnanthus angolense (Welw.) Exell. 4 0,59 Scorodophloeus zenkeri Harms 4 0,59 Staudtia kamerounensis Warb. 4 0,59 Symphonia globulifera L. 4 0,59 Ceiba pentandra Gaertn. 3 0,45 Duboscia viridiflora (K. Schum.) Mildbr. 3 0,45 Homalium longistylum Mast. 3 0,45 Oncoba welwitschii Oliv. 3 0,45 Strombosia pustulata Oliv. 3 0,45 Tabernaemontana crassa Benth. 3 0,45 Tetrapleura tetraptera (Thonn.) Taub. 3 0,45 Carapa procera DC. 2 0,29 Cola diversifolia 2 0,29 Cola griseiflora De Wild. 2 0,29 Hallea stipulosa (DC.) Leroy 2 0,29 Mammea africana Sabine 2 0,29 Ongokea gore (Hua) Pierre 2 0,29 Vitex welwitschii Gurke 2 0,29 Cola acuminata (P. Beauv.) Schott 1 0,15 Croton sylvaticus Hechst. ex Krauss 1 0,15 Diospyros crassiflora Hiern 1 0,15 Funtumia africana (Benth.) Stapf 1 0,15 Irvingia robur Mildbr. 1 0,15 Olax sp 1 0,15 Piptadeniastrum africanum (Hook. f.) Brenan 1 0,15 Ricinodendron heudelotii (Baill.) Pierre ex Heckel 1 0,15 Strombosia sp 1 0,15

From the Table 2, it is clearly shown that boonei (1.78%), Hollarhena floribunda (1.49%). Brachystegia laurentii (70.09%) was the most While Cola acuminata, Croton sylvaticus, abundant species identified, followed by Diospyros crassiflora, Funtumia africana, Irvignia Chrysophyllum lacourtianum (2.33%), Alstona robur, Olax sp., Piptadeniastrum africanum,

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Ricinodendron heudelotii and Strombosia sp. It was observed that within the Kiyaka forest were the least represented species. massif, different classes of dbh were designed according to the number of plants and/or It was observed that the highest relative density stems/ha obtained. There was an abundance of were obtained in the following families: Fabaceae species in the following classes: 10 - 29.9; 30- and Malvaceae families (14.58%) while the 49.9; and 50 - 69.9, knowing the 10-29.9 cm lowest values of the frequency was observed in class was the most representative with 580 the following families: Lecythidaceae, specimens (85%) while classes representing Irvingiaceae, Cecropiaceae (2.08%). emerging species (≥ 80 cm) have only 21 stems/ha. Eleven specimens had the diameter of 3.3 Basal Area ≥ 110 while 7 specimens were located in the class of 70-89.9. The highest basal area values were obtained in Brachystegia laurentii at 15.38 m2/ha; then The most important dbh was observed in Prioria followed by Entandrophragma utile, Prioria balsamifera (162.42 cm), the smallest in Alstonia balsamifera and Canarium schweinfurthii with boonei, Canarium schweinfurthii and Sterculia 5.43; 5.11 and 3.81 m2/ha respectively. The tracagantha with 10.19 cm for each; the average lowest basal area values were obtained in tree dbh is 80.30 cm. Croton sylvaticus and Diospyros crassiflora with 0.02 m2/ha each. The total basal area is 54 3.5 AGB, Sequestered Carbon Stock and m2/ha. Carbon Equivalent

3.4 The Distribution of Trees according to The findings of the AGB, stored carbon stock and Diameter Classes carbon equivalent obtained in this forest massif are presented in Table 1. The highest values The structure of a forest stand depends on the obtained for these three parameters were distribution of the number of its species observed in Brachystegia laurentii (651.44; according to diameter (or circumference) 306.18 and 1122, 75 t/ha), Entandrophragma classes. Thus, the distribution of species utile (355.15; 166.85 and 612.09 t/ha) and according to the diameter classes is presented in Prioria balsamifera (330.59; 155.38 and 569.77 the Fig. 3. t/ha).

16 14 12 10 8 6 4 2 0 ceae ceae Faba Ulmaceae Olacaceae Meliaceae Salicaceae Rubiaceae Clusiaceae Malvaceae Ebenaceae Sapotaceae Irvingiaceae Burceraceae Annona Verbenaceae Apocynaceae Cecropiaceae Myristicaceae Lecythidaceae Euphorbiaceae

Fig. 2. Relative density of listed families

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80

60

40 Frequency (%) Frequency

20

0 10-29.9 30-49.9 50-69.9 70-89.9 90-109.9 110 +

Diameter classes

Fig. 3. Distribution of trees according to the diameter classes (dbh)

3.6 Ecological Spectra (r2), i.e. ≥ 0.900 and the p-value ≤ 0.001. This leads to the conclusion that there is a correlation The different values of the raw ecological spectra between the two parameters concerned (Table (BT, DT and FT) of the species at dbh ≥ 10 cm 4). listed were determined according the correlation done between different parameters, of which: 3.8 Correlation between AGB and Density The AGB and the basal area. It was observed that Mesophanerophytes, sarcochores and The correlation between AGB and the density is mesophyls are more abundant 68.75, 66.67 and presented in the Table 4. 72.92% respectively. Microphanerophytes (10.42%) are the most poorly represented while The findings show that the AGB is not megaphanerophytes are relatively well approximately proportional to the diversity. The represented. Barochores and pleochores, parameters considered in this way are not macrophyles and microphylls are the rare significantly correlated. The p-values from all diaspore and foliar types found in the listed flora. equations are greater than 0.05 i.e. there is no According to the chorology, the Guinean base correlation between these two parameters. element represented 83.8% out of all species found in the florule studied. Species with a wide 3.9 Discussion geographical distribution (At and Pan) are rare, with 2.08% for each of these groups. The floristic inventory carried out in the forest massif of Kwilu province precisely at Kiyaka 3.7 Correlation between AGB and Basal station was limited to trees only, of which the dbh Area measured at 1.30m above the ground starting from trees having the dbh ≥ 10 cm. This The correlation between AGB and basal area is methodology enabled the census of 682 presented in Table 3. individuals distributed into 48 plant species, 19 families and 11 orders. The proportionality relationship between the AGB and basal area from the Chave and Pearson Among the families listed, the Fabaceae and model shows that the highest AGB value is the Malvaceae have a large number of individuals highest basal area value and the lowest AGB (29.16%). These findings are in the same value is the lowest basal area value. Thus, the magnitude as those obtained in previous studies linear, logarithmic, quadratic and cubic equations in the phytocenosis of the same station [10]. gave acceptable coefficients of determination Contrary to what has been observed in the same

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Table 3. Relation between AGB and basal area

Equation Different models Parameter estimation r2 F ddl1 ddl2 p-value Constant b1 b2 b3 Linear 0,999 355,134 1 4 0,000 87,383 8,699 Logarithmic 0,909 88,232 1 2 0,000 630,276 290,018 Quadratic 0,938 559,780 2 3 0,000 40,990 5,840 0,068 Cubic 0,918 662,472 2 2 0,002 30,990 4,840 0,068 0,000

Table 4. Correlation between AGB and density

Equation Different models Parameter estimation r2 F ddl1 ddl2 p-value Constant b1 b2 b3 Linear 0.889 335,122 1 0 0,000 665,994 97,765 Logarithmic 0.978 43,379 3 1 0,001 557,342 33,010 Quadratic 0.787 203,643 4 1 0,000 99,874 40,112 0,063 Cubic 0.676 571,89 2 2 0,001 40832 64,376 0,099 0,001 region, the families of Rubiaceae, Annonaceae stalks/ha (70.09%), while the families of and Sapotaceae are among the poorly Annonaceae, Ebenaceae, Irvingiaceae, represented [7,11]. The rarity of species Lecythidaceae, Cecropiaceae, Rubiaceae, observed within these families could be Sapotaceae, Ulmaceae and Verbenaceae are explained by the fact that our study only took into the poorest with respectively One foot/ha. account species at dbh ≥ 10 cm. Indeed, these families are made up in the majority of cases of These results differ from those obtained by trees (Sapotaceae) and/or sub-shrubs, shrubs Masens [7,11] in the same region. This rarely trees for Annonaceae and Rubiaceae difference could be explained by the fact that the families. first phytocenoses are located in the lowlands while the author's is located at the top of a hill. The forest density values of the INERA/Kiyaka With regard to basal area, Malaisse [17] station, a relatively hygrophilic lowland indicated that it is a commonly used parameter phytocenosis, are the highest compared to those for distinguishing forest formations on land. The observed in the forest massifs of Kamaba and basal area value obtained in the studied Nzundu [7,11], located on slopes (varying phytocenosis is 54.21 m2/ha. Compared to basal between 3 and 5%) and/or at the top of hills. The area values obtained in some phytocenoses values of this parameter, obtained in this studied and located in some intertropical phytocenosis, are also higher than those countries, it is clear that the land areas of obtained by Sokpon [12] in the Strombosia equatorial dense forests are generally between glauscens and Triplochiton scleroxylon forest in 23 and 50 m2/ha. Although slightly higher, this Benin, Pierlot [13] in the Scorodophloeus zenkeri value is nevertheless in the same order of forest (Lubilu, Itasukulu and Lusambila). magnitude. However, it is lower than that However, they are lower in the dry dense forest obtained by Golley et al. [18] in the riparian forest at Celtis sp (Lamto) and in the dry dense forest in Panama, at 59.6 m2/ha. As for the distribution at Anogerssus leiocarpus in Côte d'Ivoire, by of trees in diameter classes, several studies Lejoly and Sonké [14] in the Dja reserve in reported that the structure of a forest stand Cameroon and Lejoly [15] in the Ngoto forest in corresponds to the distribution of the number of the Central African Republic. Rollets [16], its trees according to diameter or circumference inventories give values ranging from 372 classes [13,19]. vines/ha in the Banco forest in Côte d'Ivoire to 546 vines/ha in Gabon (1st ha, Camp A4) for The different values observed for the first two Africa; 321 plants/ha in the Ducke reserve classes of diameter (10-29.9 cm and 30-49.9 cm) (Brazil) to 581 plants/ha in Caxuana for America; have a high proportion of individuals, most of while in Asia, this author obtains values ranging them have a dbh between 10 and 49.9 cm and from 449 plants/ha in Kalimantan Timur they are mostly shrubs. Species of which the dbh Samarinda to 677 plants/ha in Sumatra measured at 1.30 m from the ground in the other (Perawang n° 2). Among the families listed, the 4 classes have a high proportion of large trees. Fabaceae are the best represented with 471 Their distribution follows a curve whose

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concavity is turned upwards, which testifies, the 4. Bowman DMJS, Balch J, Artaxo P, Bond presence of a large number of shade species. WJ, Cochrane MA, D’Antonio CM, DeFries This shows that the dbh of trees increases with R, Johnson FH, Keeley JE, Krawchuk MA, the evolution of vegetation. This corroborates Kull CA, Mack M, Moritz MA, Pyne S, Roos with Pierlots [13] study on humic forests of CI, Scott AC, Sodhi NS, Swetnam TW, medium altitude in Zaire. The production of AGB Whittaker. The human dimension of fire contained in a given species depends on its regimes on Earth. Journal of growth in thickness. Thus, the larger the Biogeography. 2011;38(12):2223-2236. diameter, the greater the AGB increases. This 5. FAO. Challenges and opportunities for explains the AGB values, the highest obtained carbon sequestration in grassland in individuals of the Brachystegia laurentii systems: A technical report on grassland species. management and climate change mitigation. Integrated Crop Management. 4. CONCLUSION 2010;9:1-67. 6. Gibbs HK, Brown S, Nikes JO, Foley JA. This study provided information not only on the Monitoring and estimating tropical forest current floristic composition of the lowland forest carbon stocks: Making REDD a reality. massif of the INERA/Kiyaka reserve, but also on Environmental Research Letters. the aerial biomass, the basal area produced and 2007;2(4):1-13. the amount of sequestered carbon and its carbon 7. Masens da-Musa YB. Contribution to the equivalent. phytoecological study of the Kamaba forest (Kipuka, Kwilu district, Prov. De Bandundu, The values obtained from the parameters studied DR Congo); 2015. allow us to conclude that the forest massif of this 8. Belesi H. Floristic, phytogeographic and reserve plays a significant role in mitigating the phytosociological study of the vegetation of global warming currently observed throughout Bas-Kasaï in the Democratic Republic of the world and therefore has a slight impact on Congo. Unpublished PhD Dissertation, reducing the rate of greenhouse gases, Faculty of Sciences, University of particularly C02. However, this reserve is Kinshasa. 2009;565. threatened by its inhabitants on all sides; they do 9. Habari MJM. Floristic, phytogeographic want to clear large areas of forest for their field and phytosociological study of the work. It is therefore urgent for INERA/Kiyaka vegetation of Kinshasa and the middle officials to take the necessary measures for its basins of the N’Sele rivers in the full protection. Democratic Republic of Congo. Unpublished PhD Dissertation, Faculty of COMPETING INTERESTS Sciences, University of Kinshasa. 2009;272. Authors have declared that no competing 10. Masens DMYB. Phytosociological study of interests exist. the Kikwit region (Bandundu, Zaire). Unpublished PhD Dissertation, Free REFERENCES University of Brussels. 1997;382. 11. Masens YBDM, Ngbolua KN, Masens M, 1. Malhi Y, Adu-Bredu S, Asare RA, Lewis Tembeni MT, Gédéon NB. Phytoecological SL, Mayaux P. African rainforests: Past, study of Nzundu massif forest of Imbongo present and future. Philosophical city, Kwilu Province, Democratic Republic Transactions of the Royal Society of of the Congo. Tropical Plant Research. London: Biological Society. 2017;4(3):363-375. 2013;368(1625):20120312. 12. Sokpon N. Ecological research on the 2. Favero A, Daigneault A, Sohngen B. dense semi-deciduous forest of Pobé in Forests: Carbon sequestration, biomass South-Eastern Benin: Plant groups, energy or both? Science Advances. structure, natural regeneration and litter 2020;6(13):eaay6792. fall. Unpublished PhD Dissertation, Free 3. Kiage LM. Perspectives on the assumed University of Brussels. 1995;365. causes of land degradation in the 13. Pierlot R. Structure and composition of the rangelands of Sub-Saharan Africa. dense forests of Central Africa, especially Progress in Physical Geography: Earth those of Kivu. Ac. Roy. Sc. Overseas, Cl. and Environment. 2013;37(5):664-684. Sc. Nat. & Med. 1966;16:363.

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14. Lejoly J, Sonké B, Van Essche K. Use of Science, University of Toulouse, CNRS. the transect method for the study of 1969;A02:969. biodiversity in the Dja Faunal Reserve 17. Malaisse F. Contribution to the study of the (Cameroon). The biodiversity of African dry dense forest ecosystem (Muhulu). 4. plants. Proceeding XIVth AETFAT Structure of a dense dry Zambezian Forest Congress 22-27 August 1994, near Lubumbashi (Zaire). Bulletin de la Wageningen, The Netherlands. 1994;150- Société Royale de Botanique de Belgique. 154. 1984;117:428-458. 15. Lejoly J. Use of the transect method for the 18. Golley FB, McGinnis JT, Clément RG, study of biodiversity in the Ngotto Forest Child GI, Duver MJ. The structure of Conservation Area (RCA), Technical tropical forests in Panama and Colombia. Report. ECOFAC-Component RCA Bioscience. 1969;19(8):606-693. project. AGRECO-CTFT Group. 1995; 19. Rollet B, Caussinus H. On the use of a 114. mathematical model for the study of the 16. Rollet B. Quantitative studies of a dense structures of dense evergreen lowland evergreen forest in Venezuelan Guyana. rainforests. Compte Rendu de l’Académie Unpublished PhD Dissertation, Faculty of des Sciences. 1969;268:1853-1855. ______© 2020 Masens et al.; This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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